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    <abstract>Language agents are increasingly used as social simulators, but most are evaluated by local utterance plausibility rather than their ability to reproduce longitudinal discourse dynamics. We introduce PRISM, a calibrated language-agent framework for forecasting and counterfactual simulation of social media threads. PRISM separates three components that are often conflated in prompt-only agents: latent affective evolution, participation control, and language generation. It models affect with a profile-conditioned stochastic differential equation, selects participation through a partially observed activation policy, and uses a language model as a controlled generator conditioned on the resulting state. Agent profiles are instantiated with MBTI-like anchors, but treated only as behavioral priors rather than psychological ground truth. On 47,207 post-rooted discussion threads spanning 159 trending topics and 356,593 hierarchical comments, PRISM outperforms homogeneous, random, continuous-trait, prompt-only, and memory-only baselines in trajectory alignment, held-out thread forecasting, and blind human evaluation. Reliability analysis further validates LLM-derived weak labels against human and independent-model annotations. These results suggest that calibrated state-space agents offer a more reliable foundation for online discourse simulation than persona prompting alone.</abstract>
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%0 Conference Proceedings
%T Can Persona-Conditioned Language Agents Simulate Counterfactual Online Discourse Dynamics?
%A Lu, Zhixiang
%A Zheng, Botao
%A Deng, Xueyuan
%A Zhang, Chong
%A Hu, Peiyu
%A Zhou, Mian
%A Su, Jionglong
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F lu-etal-2026-persona
%X Language agents are increasingly used as social simulators, but most are evaluated by local utterance plausibility rather than their ability to reproduce longitudinal discourse dynamics. We introduce PRISM, a calibrated language-agent framework for forecasting and counterfactual simulation of social media threads. PRISM separates three components that are often conflated in prompt-only agents: latent affective evolution, participation control, and language generation. It models affect with a profile-conditioned stochastic differential equation, selects participation through a partially observed activation policy, and uses a language model as a controlled generator conditioned on the resulting state. Agent profiles are instantiated with MBTI-like anchors, but treated only as behavioral priors rather than psychological ground truth. On 47,207 post-rooted discussion threads spanning 159 trending topics and 356,593 hierarchical comments, PRISM outperforms homogeneous, random, continuous-trait, prompt-only, and memory-only baselines in trajectory alignment, held-out thread forecasting, and blind human evaluation. Reliability analysis further validates LLM-derived weak labels against human and independent-model annotations. These results suggest that calibrated state-space agents offer a more reliable foundation for online discourse simulation than persona prompting alone.
%U https://aclanthology.org/2026.emnlp-main.1733/
%P 39545-39562

Markdown (Informal)

[Can Persona-Conditioned Language Agents Simulate Counterfactual Online Discourse Dynamics?](https://aclanthology.org/2026.emnlp-main.1733/) (Lu et al., EMNLP 2026)

• Can Persona-Conditioned Language Agents Simulate Counterfactual Online Discourse Dynamics? (Lu et al., EMNLP 2026)

ACL

• Zhixiang Lu, Botao Zheng, Xueyuan Deng, Chong Zhang, Peiyu Hu, Mian Zhou, and Jionglong Su. 2026. Can Persona-Conditioned Language Agents Simulate Counterfactual Online Discourse Dynamics?. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 39545–39562, Budapest, Hungary. Association for Computational Linguistics.